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The commuting origin-destination~(OD) matrix is a critical input for urban planning and transportation, providing crucial information about the population residing in one region and working in another within an interested area.
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Filippo Simini, Marta C González, Amos Maritan, and Albert-László Barabási · 2012
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U.S. Census Bureau · 2012
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Md Shahadat Iqbal, Charisma F Choudhury, Pu Wang, and Marta C González · 2014
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Chen Zhong, Stefan Müller Arisona, Xianfeng Huang, Michael Batty, and Gerhard Schmitt · 2014
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A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks
Meead Saberi, Hani S Mahmassani, Dirk Brockmann, and Amir Hosseini · 2017
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Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Enhancing trip distribution prediction with twitter data: comparison of neural network and gravity models
Nastaran Pourebrahim, Selima Sultana, Jean-Claude Thill, and Somya Mohanty · 2018
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A machine learning approach to modeling human migration
Learning geo-contextual embeddings for commuting flow prediction
Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, and Claudio Silva · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Spatial origin-destination flow imputation using graph convolutional networks
Xin Yao, Yong Gao, Di Zhu, Ed Manley, Jiaoe Wang, and Yu Liu · 2020
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Origin-destination trips generated from operational data of a mobile network for urban transportation planning
Ryuichi Imai, Daizo Ikeda, Hiroyasu Shingai, Tomohiro Nagata, and Koichi Shigetaka · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Caleb Robinson and Bistra Dilkina · 2018
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A complex network methodology for travel demand model evaluation and validation
Meead Saberi, Taha H Rashidi, Milad Ghasri, and Kenneth Ewe · 2018
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Using google’s passive data and machine learning for origin-destination demand estimation
Bhargava Sana, Joe Castiglione, Drew Cooper, and Dan Tischler · 2018
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Trip distribution modeling with twitter data
Nastaran Pourebrahim, Selima Sultana, Amirreza Niakanlahiji, and Jean-Claude Thill · 2019
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Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling
Yuandong Wang, Hongzhi Yin, Hongxu Chen, Tianyu Wo, Jie Xu, and Kai Zheng · 2019
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Origin–destination matrix estimation and prediction from socioeconomic variables using automatic feature selection procedure-based machine learning model
PJ Rodriguez-Rueda, JJ Ruiz-Aguilar, J Gonzalez-Enrique, and I Turias · 2021
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A deep gravity model for mobility flows generation
Filippo Simini, Gianni Barlacchi, Massimilano Luca, and Luca Pappalardo · 2021
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Causal learning empowered od prediction for urban planning
Jinwei Zeng, Guozhen Zhang, Can Rong, Jingtao Ding, Jian Yuan, and Yong Li · 2022
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An interdisciplinary survey on origin-destination flows modeling: Theory and techniques
Can Rong, Jingtao Ding, and Yong Li · 2023
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Complexity-aware large scale origin-destination network generation via diffusion model
Can Rong, Jingtao Ding, Zhicheng Liu, and Yong Li · 2023
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Goddag: generating origin-destination flow for new cities via domain adversarial training
Can Rong, Jie Feng, and Jingtao Ding · 2023
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Origin-destination network generation via gravity-guided gan
Can Rong, Huandong Wang, and Yong Li · 2023
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Urban dynamics through the lens of human mobility
Yanyan Xu, Luis E Olmos, David Mateo, Alberto Hernando, Xiaokang Yang, and Marta C Gonzalez · 2023
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